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Guitar Tablature Transcription using a Deep Belief Network

  • Author / Creator
    Burlet, Gregory D.
  • Music transcription is the process of extracting the pitch and timing of notes that occur in an audio recording and writing the results as a music score, commonly referred to as sheet music. Manually transcribing audio recordings is a difficult and time-consuming process, even for experienced musicians. In response, several algorithms have been proposed to automatically analyze and transcribe the notes sounding in an audio recording; however, these algorithms are often general-purpose, attempting to process any number of instruments producing any number of notes sounding simultaneously. This work presents a transcription algorithm that is constrained to processing the audio output of a single instrument, specifically an acoustic guitar. The transcription system consists of a novel note pitch estimation algorithm that uses a deep belief network and multi-label learning techniques to generate multiple pitch estimates for each segment of the input audio signal. Using a compiled dataset of synthesized guitar recordings for evaluation, the algorithm described in this work results in a 12% increase in the f-measure of note transcriptions relative to a state-of-the-art algorithm in the literature. This thesis demonstrates the effectiveness of deep, multi-label learning for the task of guitar audio transcription.

  • Subjects / Keywords
  • Graduation date
    2015-11
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R3513V698
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Computing Science
  • Supervisor / co-supervisor and their department(s)
    • Hindle, Abram (Computing Science)
  • Examining committee members and their departments
    • Smallwood, Scott (Music)
    • Hindle, Abram (Computing Science)
    • Boulanger, Pierre (Computing Science)
    • Schuurmans, Dale (Computing Science)